The DEFACTO system is a multiagent based tool for training incident commanders for large scale disasters. In this paper, we highlight some of the lessons that we have learned from our interaction with the Los Angeles Fire Department (LAFD) and how they have affected the way that we continued the design of our training system. These lessons were gleaned from LAFD feedback and initial training exercises and they include: system design, visualization, improving trainee situational awareness, adjusting training level of difficulty and situation scale. We have taken these lessons and used them to improve the DEFACTO system's training capabilities. We have conducted initial training exercises to illustrate the utility of the system in terms of providing useful feedback to the trainee.
%0 Conference Paper
%1 conf/aamas06/Schurr
%A Schurr, Nathan
%A Patil, Pratik
%A Pighin, Fred
%A Tambe, Milind
%B AAMAS '06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
%C New York, NY, USA
%D 2006
%I ACM Press
%K aamas06 adjustableautonomy demo hmi rescue
%P 1490--1497
%R http://doi.acm.org/10.1145/1160633.1160924
%T Using multiagent teams to improve the training of incident commanders
%U http://portal.acm.org/citation.cfm?id=1160633.1160924&coll=ACM&dl=ACM&type=series&idx=1160633&part=Proceedings&WantType=Proceedings&title=International%20Conference%20on%20Autonomous%20Agents&CFID=25754815&CFTOKEN=92537800
%X The DEFACTO system is a multiagent based tool for training incident commanders for large scale disasters. In this paper, we highlight some of the lessons that we have learned from our interaction with the Los Angeles Fire Department (LAFD) and how they have affected the way that we continued the design of our training system. These lessons were gleaned from LAFD feedback and initial training exercises and they include: system design, visualization, improving trainee situational awareness, adjusting training level of difficulty and situation scale. We have taken these lessons and used them to improve the DEFACTO system's training capabilities. We have conducted initial training exercises to illustrate the utility of the system in terms of providing useful feedback to the trainee.
%@ 1-59593-303-4
@inproceedings{conf/aamas06/Schurr,
abstract = {The DEFACTO system is a multiagent based tool for training incident commanders for large scale disasters. In this paper, we highlight some of the lessons that we have learned from our interaction with the Los Angeles Fire Department (LAFD) and how they have affected the way that we continued the design of our training system. These lessons were gleaned from LAFD feedback and initial training exercises and they include: system design, visualization, improving trainee situational awareness, adjusting training level of difficulty and situation scale. We have taken these lessons and used them to improve the DEFACTO system's training capabilities. We have conducted initial training exercises to illustrate the utility of the system in terms of providing useful feedback to the trainee.},
added-at = {2007-08-03T15:04:26.000+0200},
address = {New York, NY, USA},
author = {Schurr, Nathan and Patil, Pratik and Pighin, Fred and Tambe, Milind},
biburl = {https://www.bibsonomy.org/bibtex/231df39e6bd009809f10cc430393db630/mpfingst},
booktitle = {AAMAS '06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems},
description = {: AAMAS '06, Using multiagent teams to ...},
doi = {http://doi.acm.org/10.1145/1160633.1160924},
interhash = {72438f788480221a7823212e9e973f39},
intrahash = {31df39e6bd009809f10cc430393db630},
isbn = {1-59593-303-4},
keywords = {aamas06 adjustableautonomy demo hmi rescue},
location = {Hakodate, Japan},
pages = {1490--1497},
publisher = {ACM Press},
timestamp = {2007-08-03T15:04:26.000+0200},
title = {Using multiagent teams to improve the training of incident commanders},
url = {http://portal.acm.org/citation.cfm?id=1160633.1160924&coll=ACM&dl=ACM&type=series&idx=1160633&part=Proceedings&WantType=Proceedings&title=International%20Conference%20on%20Autonomous%20Agents&CFID=25754815&CFTOKEN=92537800},
year = 2006
}